Novel algorithms for extraction of network models from multidimensional genome-scale data
We are interested in development of novel computational algorithms for automated extraction of complete network models from multidimensional genome-scale data, such as transcriptomics, copy number data, methylomics, metabolomics and proteomics. We have developed a biased random walk method, NetWalk, for effective scoring and querying of molecular networks from genome-scale data. NetWalk has been used in multiple projects in our own lab and in collaboration with others. We are currently working on extending NetWalk to analyses of multi-scale abstract networks for integration of multiple data types to build semi-abstract and interactive network models.
Figure 1. Comparison of A) list-based methods of network construction and B) NetWalk. In list-based network construction, interacting genes (open nodes) are added to the network of seed nodes (red) to connect them together. This will generate a single or a number of networks of interest. Distribution of data values of interactor nodes are random. In contrast, NetWalk transforms gene-centric data to interaction-centric data, which can be used for standard statistical analyses (e.g. heatmap analyses) or for dynamic network construction. Data values of nodes constructed through EF values are coherent with input values.
Developing NetWalker: a software platform for network analysis in functional genomics
We are developing a software suite, NetWalker, as a freely available tool for the research community for conducting network-based analyses of genomic data.
features standard methods for basic data handling and processing, clustering and heatmap analyses; but more importantly, it features advanced methods for network-based analyses of genomic data in a highly interactive visual environment.
Analyses of molecular networks of multicellular communities
is shaped by the multicellular environment it resides in. To facilitate delineation of the molecular structure of the multicellular environment, we have extended our NetWalk algorithm to analyses of genomic data from multicellular systems. Using this method, we can analyze the molecular networks of multicellular communities that shape the collective phenotypic behavior of the whole multicellular system. We are currently engaged in a collaboration with Nancy Ratner's lab in the analyses of molecular networks in the highly heterogeneous multicellular microenvironment of Neurofibroma tumors.
Figure 3. Community molecular network (CMN) analysis of tumor and stromal gene expression in drug resistance. A) Highest scoring interactions from the NetWalk-based CMN analysis of tumor and stromal gene expression. Nodes are colored according to gene expressions in tumor or stromal cells. Some subnetworks of intercellular interactions are highlighted in boxes. Gray interactions are protein-protein, and orange interactions are metabolic. B) A blow-up of the network 1 in A. C) A blow-up of the network 7 in A. D) Metabolic re-actions performed by enzymes in C and in subnetwork 9 in the tumor and stromal cells. Note a potential metabolic symbiosis between tumor and stromal cells in drug resistance involving division of glycolysis (e.g. glucose uptake, lactate production) and gluconeogenesis (lactate oxidation, pyruvate synthesis) between tumor and stromal cells, respectively.